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CHI ‘94 Card, Pirolli, & Mackinlay PowerPoint Presentation
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CHI ‘94 Card, Pirolli, & Mackinlay

CHI ‘94 Card, Pirolli, & Mackinlay

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CHI ‘94 Card, Pirolli, & Mackinlay

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  1. The Cost-of-Knowledge Characteristic Function:Display Evaluation for Direct-Walk Dynamic Information Visualizations CHI ‘94 Card, Pirolli, & Mackinlay

  2. Concepts • Information has cost structure • Objective: maximize information benefits per unit cost (cost ~ time) • Cost-of-Knowledge Characteristic Function: • Characterizes the effect of a design of dynamic display/human-computer dialogue on information’s cost structure

  3. Cost-of-Knowledge Characteristic Function • Improve productivity: Less time or more output

  4. Case study • Direct-walk interactive infoviz • Navigate an information structure using mouse points/other direct manipulation methods • Analyze 2 calendar programs: Spiral Calendar vs. Sun’s CM • Users: 4 users for each study, 2 overlapping • Task: navigate to another day in calendar

  5. Steps To Construct Cost-of-Knowledge Function • Use tasks that take different amount of time to obtain different amount of information • Identify cost drivers for the tasks: • In Spiral Calendar: # of cycles to go through • In CM: # of different steps • Measure time taken to perform each task as cost • Perform regression of time (cost) as dependent variable and cost drivers as independent variables • Plot cost vs. amount of information that can be obtained

  6. Cost Drivers • Spiral Calendar:Number of display cycles (Century, Decade, Year, Month, Week, Day) selected • Regression fn : Time = 3.3 + 3.5 * Ncycles • CM: • m: point, menu pull-down, & select • P: point & select • B: press a button • Regression fn: Time = 1.3 + 3.9 m + 1.4 P + 0.36 B

  7. Spiral Calendar Result Computation

  8. Sun CM Result Computation

  9. Cost-of-Knowledge Functions

  10. Value of Tasks • Values of tasks: • Frequency • Importance • Etc. • Needs to weight tasks by their values • Ex. Use probability density function to weight tasks by frequency of use: Pr{needed|D days ago}=0.34/(0.34+D0.83)

  11. Expected Probability-Weighted Costs

  12. Summary • More measurable/computable method to evaluate a design • Know your priority/objective: sometimes perceived speed is more important than actual speed • Issues: • How to accurately identify and measure all cost drivers of a task, e.g. # of items? • What if there are more than one way to perform a specific task?

  13. The WebBook & Web Forager: An Information Workspace for the World-Wide Web CHI ’96 Card, Robertson, and York

  14. WebBook & Web Forager • Two related designs • WebBook - 3D interactive book of HTML pages • Web Forager – an application that puts WebBook and other objects in a 3D hierarchical workspace

  15. Based On… • Cost structure of information workspaces – the web has a uniform cost structure • Information foraging theory – users often seek strategies to increase the encounter rates of relevant information • Locality of reference – users tend to interact repeatedly with small clusters of information, and therefore keeping the cost of accessing low

  16. Problems At That Time… • Hotlist – still have to wait for slow access times, not tunable to a reasonably cost-structured workspace. • Multiple windows slow users down since they overlap. • Users can only be at one page while the way the users actually work with information is to have multiple pages simultaneously available at hand.

  17. WebBook • Use book metaphor (animated 3D): next& previous links analogous to books, familiar, effective display • Any collection of preload pages • Can be bookmarked, put on a shelf • Various way to collect URLs: relative-URL, Topic, Hot List, Search Reports

  18. Web Forager • Explore the potential for rapid interaction with large number of pages • Use gestures to increase speed with which objects can be moved around • Focus on the web • Use a structured model to design (CoKC Fn) • 3 levels: book/page  air & desk  bookcase

  19. Web Forager

  20. Cost of Knowledge Characteristic Function for Web Forager

  21. Comments • Metaphor – do they really take advantage of the affordances of a physical book and workspace? What might you lose from using this metaphor? • Speed of retrieving a web page is becoming less an issue • Current browsers might already be able to solve the problems posed by the authors (and even work better, perhaps!)

  22. Effective View Navigation CHI ’97 Furnas

  23. Effective View Navigation ContextNavigate an information structure by selecting something in the current view of the structure Problems • Large structures • Limited resources of space & time Proposed RequirementsEffective View Navigation (EVN) = Effective View Traversibility (EVT) + View Navigability (VN)

  24. Terms • View traversal: iterative process of viewing, selecting, & moving to it • View navigation: decide where to go next • Logical graph: logical structure of the information • Viewing graph: contains nodes that users see in current view

  25. EVT Requirements for Viewing Graphs EVT1: Small Views – space constraint# of outgoing links of any node relative to structure’s size must be small  small Maximal Out-Degree (MOD) EVT2: Short paths – time constraint the longest connecting path relative to structure’s size must be small  small Diameter (DIA) EVT(G)=(MOD(G),DIA(G)) G= viewing graph

  26. A Scrolling List EVT=(O(1), O(n))

  27. A Balanced Tree EVT=(O(1), O(log n))

  28. Improving EVT of a List • More dimensions - multi-column list: EVT=(O(1), O(sqrt(n)))

  29. Improving EVT of a List • Fisheye sampling: EVT=(O(log n), O(log n)) – allow jumping further, but larger view

  30. Improving EVT of a List • Adding a tree: EVT=(O(1), O(log n)) – create categorization?

  31. EVT Summary • Present information in a representation that naturally supports EVT - tree • To fix non-EVT logical structures: • Add long-distance links • Glue with another complete EVT structure

  32. View Navigability (VN) • Ability to find good paths to targets without error & history-less

  33. Terms • Outlink-info: info associated with outlink of a node (enumeration or labeling) • To-set: all possible targets a link actually leads to • Inferred-to-set: targets that the outlink-info seem to indicate • Residue/scent: remote indication of a node/target • Well-matched outlink info: inferred-to-set implies to-set

  34. Illustration

  35. Strong Navigability Requirement • Outlink-info must be everywhere well-matched • Every node must have good residue at every other node • Outlink-info must be small, but need to describe the whole to-set, not just the next node (e.g. highway signs) • Semantic labeling that mirrors actual partition of to-sets Targets share residue

  36. Good Example • Systematic labeling of trees of hierarchy, i.e. biological taxonomy

  37. Non-Navigable Structure • Completely unrelated/unstructured items • Only works with enumeration • Locally-related structure – no good residue for things far away, e.g. WWW • Combine query & navigation

  38. Combining EVT + VN • Large scale semantics (structure with larger groups) work! • Assume n nodes & v links in the structure • Small view & diameter: v should be small compared to n • Average size of to-sets (n/v) should be large • Carve up the semantics of the domain efficiently – due to Small Diameter req. • Small # of intersections • Balanced hierarchy

  39. Summary • Effective view navigation: • Small views • Reasonable # of steps to move around • Discoverable route to any target • Do navigability requirements guarantee users to always find shortest paths?